Computational limitations in robust classification and win-win results
Computational limitations in robust classification and win-win results.
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Computational limitations in robust classification and win-win results.
We’ve discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural network training on a wide range of tasks. Since complex tasks tend to have...
We’re releasing CoinRun, a training environment which provides a metric for an agent’s ability to transfer its experience to novel situations and has already helped clarify a longstanding puzzle...
We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning. Spinning Up consists of crystal-clear...
We’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as near, above, between, closest, and furthest, expressed as sets of 2d points. ...
Plan online, learn offline: Efficient learning and exploration via model-based control.
We’ve developed Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time...
We’re proposing an AI safety technique called iterated amplification that lets us specify complicated behaviors and goals that are beyond human scale, by demonstrating how to decompose a task into...
We are now accepting applications for our second cohort of OpenAI Scholars, a program where we provide 6–10 stipends and mentorship to individuals from underrepresented groups to study deep...
We are now accepting applications for OpenAI Fellows and Interns for 2019.
FFJORD: Free-form continuous dynamics for scalable reversible generative models.
Our first cohort of OpenAI Scholars has now completed the program.
OpenAI Five lost two games against top Dota 2 players at The International in Vancouver this week, maintaining a good chance of winning for the first 20–35 minutes of both games.
Large-scale study of curiosity-driven learning.
Yesterday, OpenAI Five won a best-of-three against a team of 99. 95th percentile Dota players: Blitz, Cap, Fogged, Merlini, and MoonMeander—four of whom have played Dota professionally—in front of...
We’ve trained a human-like robot hand to manipulate physical objects with unprecedented dexterity.
Variational option discovery algorithms.
The OpenAI Five Benchmark match is now over!
We introduce Glow, a reversible generative model which uses invertible 1x1 convolutions. It extends previous work on reversible generative models and simplifies the architecture.
We’ve trained an agent to achieve a high score of 74,500 on Montezuma’s Revenge from a single human demonstration, better than any previously published result. Our algorithm is simple: the agent...
Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2.
The first run of our Retro Contest—exploring the development of algorithms that can generalize from previous experience—is now complete.
Learning policy representations in multiagent systems.
We’ve obtained state-of-the-art results on a suite of diverse language tasks with a scalable, task-agnostic system, which we’re also releasing. Our approach is a combination of two existing...
GamePad: A learning environment for theorem proving.
We’re now accepting applications for the next cohort of OpenAI Fellows, a program which offers a compensated 6-month apprenticeship in AI research at OpenAI.
We’re releasing the full version of Gym Retro, a platform for reinforcement learning research on games. This brings our publicly-released game count from around 70 Atari games and 30 Sega games...
We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3. 4-month doubling time (by comparison,...
We’re proposing an AI safety technique which trains agents to debate topics with one another, using a human to judge who wins.
We’re releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. ...